ENSE 461

This course explores how artificial intelligence (AI) methods can be applied to dynamic engineering systems to enable intelligent software solutions. Students will learn how to model, analyze, and optimize the behavior of real-world systems using modern data-driven and machine learning approaches. Topics include system identification, dimensionality reduction, reinforcement learning, predictive optimization, and physics-informed neural networks (PINNs). The course emphasizes software engineering practices for integrating AI into simulations and decision-making pipelines, focusing on robustness, testing, and ethical deployment. Students will build a portfolio of applied AI software solutions relevant to robotics, automation, aerospace, smart infrastructure, and industrial systems through assignments and structured labs. ***